In animals, development and differentiation proceeds by the sequential activation of gene expression. Our primary goal is to elucidate the complex network of genetic interactions that underlies the processes of normal development, disease and evolution. A comprehensive analysis of these interactions requires knowledge of the gene expression profiles for the full complement of genes in an organism. At the Berkeley Drosophila Genome Project (BDGP), we have established a gene expression resource for Drosophila development that contains spatial and temporal embryonic expression patterns determined from whole mount RNA in-situ hybridization. These patterns are annotated using a standardized, controlled vocabulary based on an anatomical ontology and a standardized virtual representation of the patterns to facilitate image based search and analysis. This resource now includes embryonic expression patterns for all sequence-specific DNA-binding proteins or transcription factors (TFs) that control the processes of animal organogenesis. Specifically we propose to continue to collect RNA expression patterns for the remaining protein-coding genes, including newly identified genes, and expand the collection to include non-coding genes and alternative transcripts with likely embryonic expression; assay patterns of expression driven by putative CRMs for transcription factors (TFs) and analyze the expression using our newly developed computational image analysis tools; and finally compare TF RNA expression patterns to corresponding protein expression determined from GFP-tagged lines for all TFs. The gene expression data produced by our study will provide fundamental information for elucidating gene function in Drosophila and, by homology, in other eukaryotes, including humans. The roles of most non-coding RNAs in particular remain unknown. The functional analysis of regulatory regions will provide insights into the developmental roles of the transcription factors. The integration of transcript, protein and CRM spatiotemporal expression data will promote discovery of networks of regulatory interactions. These studies are directed toward the understanding of life processes and lay the foundation for promoting better human health.